Overview

Dataset statistics

Number of variables24
Number of observations10000
Missing cells3598
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory192.0 B

Variable types

Categorical7
Numeric17

Alerts

date_time has a high cardinality: 10000 distinct values High cardinality
srch_ci has a high cardinality: 967 distinct values High cardinality
srch_co has a high cardinality: 978 distinct values High cardinality
site_name is highly correlated with posa_continentHigh correlation
posa_continent is highly correlated with site_nameHigh correlation
orig_destination_distance is highly correlated with hotel_continentHigh correlation
srch_destination_id is highly correlated with srch_destination_type_idHigh correlation
srch_destination_type_id is highly correlated with srch_destination_idHigh correlation
hotel_continent is highly correlated with orig_destination_distanceHigh correlation
site_name is highly correlated with posa_continentHigh correlation
posa_continent is highly correlated with site_nameHigh correlation
srch_adults_cnt is highly correlated with srch_rm_cntHigh correlation
srch_rm_cnt is highly correlated with srch_adults_cntHigh correlation
site_name is highly correlated with posa_continentHigh correlation
posa_continent is highly correlated with site_nameHigh correlation
site_name is highly correlated with posa_continent and 2 other fieldsHigh correlation
posa_continent is highly correlated with site_name and 4 other fieldsHigh correlation
user_location_country is highly correlated with site_name and 2 other fieldsHigh correlation
user_location_region is highly correlated with site_name and 2 other fieldsHigh correlation
orig_destination_distance is highly correlated with hotel_continent and 2 other fieldsHigh correlation
srch_adults_cnt is highly correlated with srch_rm_cntHigh correlation
srch_rm_cnt is highly correlated with srch_adults_cntHigh correlation
srch_destination_id is highly correlated with srch_destination_type_idHigh correlation
srch_destination_type_id is highly correlated with srch_destination_idHigh correlation
hotel_continent is highly correlated with posa_continent and 3 other fieldsHigh correlation
hotel_country is highly correlated with posa_continent and 3 other fieldsHigh correlation
hotel_market is highly correlated with orig_destination_distance and 2 other fieldsHigh correlation
orig_destination_distance has 3582 (35.8%) missing values Missing
date_time is uniformly distributed Uniform
date_time has unique values Unique
user_location_region has 131 (1.3%) zeros Zeros
channel has 1224 (12.2%) zeros Zeros
srch_children_cnt has 7884 (78.8%) zeros Zeros
hotel_continent has 186 (1.9%) zeros Zeros

Reproduction

Analysis started2022-09-03 16:38:46.591885
Analysis finished2022-09-03 16:39:33.833413
Duration47.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

date_time
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2014-07-03 10:19:15
 
1
2013-08-04 14:42:43
 
1
2013-08-14 12:51:29
 
1
2013-02-24 22:19:54
 
1
2013-05-06 11:45:39
 
1
Other values (9995)
9995 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row2014-07-03 10:19:15
2nd row2014-07-02 18:39:51
3rd row2013-05-09 09:41:01
4th row2014-07-30 19:23:01
5th row2014-07-10 15:14:48

Common Values

ValueCountFrequency (%)
2014-07-03 10:19:151
 
< 0.1%
2013-08-04 14:42:431
 
< 0.1%
2013-08-14 12:51:291
 
< 0.1%
2013-02-24 22:19:541
 
< 0.1%
2013-05-06 11:45:391
 
< 0.1%
2014-07-10 22:44:411
 
< 0.1%
2013-06-22 17:23:481
 
< 0.1%
2014-07-11 23:06:151
 
< 0.1%
2013-07-08 21:13:421
 
< 0.1%
2014-05-28 11:50:531
 
< 0.1%
Other values (9990)9990
99.9%

Length

2022-09-03T18:39:33.937067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-12-0940
 
0.2%
2014-12-3040
 
0.2%
2014-09-2439
 
0.2%
2014-07-2138
 
0.2%
2014-12-1036
 
0.2%
2014-11-1335
 
0.2%
2014-11-0535
 
0.2%
2014-07-0935
 
0.2%
2014-12-2935
 
0.2%
2014-07-1635
 
0.2%
Other values (10049)19632
98.2%

Most occurring characters

ValueCountFrequency (%)
131067
16.4%
031063
16.3%
224068
12.7%
-20000
10.5%
:20000
10.5%
414684
7.7%
311658
 
6.1%
10000
 
5.3%
57747
 
4.1%
75129
 
2.7%
Other values (3)14584
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number140000
73.7%
Dash Punctuation20000
 
10.5%
Other Punctuation20000
 
10.5%
Space Separator10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131067
22.2%
031063
22.2%
224068
17.2%
414684
10.5%
311658
 
8.3%
57747
 
5.5%
75129
 
3.7%
94984
 
3.6%
84968
 
3.5%
64632
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
-20000
100.0%
Other Punctuation
ValueCountFrequency (%)
:20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131067
16.4%
031063
16.3%
224068
12.7%
-20000
10.5%
:20000
10.5%
414684
7.7%
311658
 
6.1%
10000
 
5.3%
57747
 
4.1%
75129
 
2.7%
Other values (3)14584
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131067
16.4%
031063
16.3%
224068
12.7%
-20000
10.5%
:20000
10.5%
414684
7.7%
311658
 
6.1%
10000
 
5.3%
57747
 
4.1%
75129
 
2.7%
Other values (3)14584
7.7%

site_name
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9692
Minimum2
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:34.070564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q317
95-th percentile37
Maximum53
Range51
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.06523138
Coefficient of variation (CV)1.210250711
Kurtosis-0.06173860975
Mean9.9692
Median Absolute Deviation (MAD)0
Skewness1.210677867
Sum99692
Variance145.5698083
MonotonicityNot monotonic
2022-09-03T18:39:34.220934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
26270
62.7%
11722
 
7.2%
24622
 
6.2%
37550
 
5.5%
34490
 
4.9%
23220
 
2.2%
8216
 
2.2%
13186
 
1.9%
1792
 
0.9%
2870
 
0.7%
Other values (29)562
 
5.6%
ValueCountFrequency (%)
26270
62.7%
62
 
< 0.1%
77
 
0.1%
8216
 
2.2%
913
 
0.1%
1022
 
0.2%
11722
 
7.2%
13186
 
1.9%
1414
 
0.1%
1519
 
0.2%
ValueCountFrequency (%)
531
 
< 0.1%
486
 
0.1%
463
 
< 0.1%
453
 
< 0.1%
441
 
< 0.1%
431
 
< 0.1%
4012
 
0.1%
37550
5.5%
3616
 
0.2%
3532
 
0.3%

posa_continent
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
3
7500 
1
1225 
2
918 
4
 
286
0
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

Length

2022-09-03T18:39:34.365239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T18:39:34.553242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

Most occurring characters

ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37500
75.0%
11225
 
12.2%
2918
 
9.2%
4286
 
2.9%
071
 
0.7%

user_location_country
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.1988
Minimum0
Maximum239
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:34.682597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q166
median66
Q370
95-th percentile205
Maximum239
Range239
Interquartile range (IQR)4

Descriptive statistics

Standard deviation59.56096397
Coefficient of variation (CV)0.6909720782
Kurtosis0.2572150546
Mean86.1988
Median Absolute Deviation (MAD)0
Skewness1.120965135
Sum861988
Variance3547.508429
MonotonicityNot monotonic
2022-09-03T18:39:34.849326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
665331
53.3%
2051153
 
11.5%
3592
 
5.9%
69524
 
5.2%
1217
 
2.2%
77216
 
2.2%
46193
 
1.9%
215139
 
1.4%
133106
 
1.1%
2391
 
0.9%
Other values (129)1438
 
14.4%
ValueCountFrequency (%)
041
 
0.4%
1217
 
2.2%
3592
5.9%
41
 
< 0.1%
520
 
0.2%
62
 
< 0.1%
82
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
1238
 
0.4%
ValueCountFrequency (%)
2391
 
< 0.1%
23531
0.3%
2331
 
< 0.1%
23140
0.4%
23010
 
0.1%
2296
 
0.1%
2282
 
< 0.1%
2224
 
< 0.1%
2211
 
< 0.1%
2201
 
< 0.1%

user_location_region
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct493
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.8758
Minimum0
Maximum1017
Zeros131
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:35.008552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1174
median314
Q3385
95-th percentile785.1
Maximum1017
Range1017
Interquartile range (IQR)211

Descriptive statistics

Standard deviation207.9108286
Coefficient of variation (CV)0.6731211336
Kurtosis1.515119939
Mean308.8758
Median Absolute Deviation (MAD)133
Skewness1.127841125
Sum3088758
Variance43226.91267
MonotonicityNot monotonic
2022-09-03T18:39:35.172803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1741063
 
10.6%
348478
 
4.8%
354463
 
4.6%
442375
 
3.8%
220375
 
3.8%
462283
 
2.8%
50278
 
2.8%
155255
 
2.5%
135212
 
2.1%
258210
 
2.1%
Other values (483)6008
60.1%
ValueCountFrequency (%)
0131
1.3%
32
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
99
 
0.1%
102
 
< 0.1%
1118
 
0.2%
ValueCountFrequency (%)
10173
 
< 0.1%
10161
 
< 0.1%
101111
0.1%
10105
0.1%
10071
 
< 0.1%
10052
 
< 0.1%
10041
 
< 0.1%
10034
 
< 0.1%
10029
0.1%
10011
 
< 0.1%

user_location_city
Real number (ℝ≥0)

Distinct3321
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27498.3059
Minimum15
Maximum56507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:35.328182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile2096
Q112802.75
median26956.5
Q342328
95-th percentile53139
Maximum56507
Range56492
Interquartile range (IQR)29525.25

Descriptive statistics

Standard deviation16777.10576
Coefficient of variation (CV)0.6101141583
Kurtosis-1.253788739
Mean27498.3059
Median Absolute Deviation (MAD)14992.5
Skewness0.02731975892
Sum274983059
Variance281471277.7
MonotonicityNot monotonic
2022-09-03T18:39:35.479229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5703215
 
2.1%
48862172
 
1.7%
24103124
 
1.2%
25315106
 
1.1%
3608694
 
0.9%
1470379
 
0.8%
3539072
 
0.7%
208670
 
0.7%
492470
 
0.7%
2623269
 
0.7%
Other values (3311)8929
89.3%
ValueCountFrequency (%)
151
 
< 0.1%
191
 
< 0.1%
401
 
< 0.1%
502
 
< 0.1%
691
 
< 0.1%
7627
0.3%
1041
 
< 0.1%
1131
 
< 0.1%
1205
 
0.1%
1331
 
< 0.1%
ValueCountFrequency (%)
565072
 
< 0.1%
564721
 
< 0.1%
5644034
0.3%
564362
 
< 0.1%
563994
 
< 0.1%
563923
 
< 0.1%
563501
 
< 0.1%
563411
 
< 0.1%
563151
 
< 0.1%
562951
 
< 0.1%

orig_destination_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6406
Distinct (%)99.8%
Missing3582
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean1958.831814
Minimum0.0603
Maximum11478.4059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:35.642655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0603
5-th percentile39.62321
Q1310.416775
median1144.79825
Q32537.7307
95-th percentile6758.862065
Maximum11478.4059
Range11478.3456
Interquartile range (IQR)2227.313925

Descriptive statistics

Standard deviation2227.854817
Coefficient of variation (CV)1.13733849
Kurtosis2.291083553
Mean1958.831814
Median Absolute Deviation (MAD)953.10285
Skewness1.626872225
Sum12571782.58
Variance4963337.086
MonotonicityNot monotonic
2022-09-03T18:39:35.803444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276.21482
 
< 0.1%
1208.70562
 
< 0.1%
2233.81912
 
< 0.1%
114.27962
 
< 0.1%
1033.36472
 
< 0.1%
560.75452
 
< 0.1%
952.72442
 
< 0.1%
11.60362
 
< 0.1%
946.67022
 
< 0.1%
189.40922
 
< 0.1%
Other values (6396)6398
64.0%
(Missing)3582
35.8%
ValueCountFrequency (%)
0.06031
< 0.1%
0.11831
< 0.1%
0.13481
< 0.1%
0.20331
< 0.1%
0.22621
< 0.1%
0.241
< 0.1%
0.24521
< 0.1%
0.25791
< 0.1%
0.2881
< 0.1%
0.31051
< 0.1%
ValueCountFrequency (%)
11478.40591
< 0.1%
11440.32571
< 0.1%
11411.00351
< 0.1%
11372.48731
< 0.1%
11225.31171
< 0.1%
11152.79211
< 0.1%
10951.35211
< 0.1%
10599.75751
< 0.1%
10549.8721
< 0.1%
10549.5861
< 0.1%

user_id
Real number (ℝ≥0)

Distinct9866
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599553.6947
Minimum243
Maximum1198741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:35.964712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum243
5-th percentile57371.95
Q1297793.5
median596155
Q3899771
95-th percentile1143234.2
Maximum1198741
Range1198498
Interquartile range (IQR)601977.5

Descriptive statistics

Standard deviation348595.4759
Coefficient of variation (CV)0.5814249482
Kurtosis-1.20764299
Mean599553.6947
Median Absolute Deviation (MAD)300574
Skewness0.01296501158
Sum5995536947
Variance1.215188058 × 1011
MonotonicityNot monotonic
2022-09-03T18:39:36.145532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003173
 
< 0.1%
10886733
 
< 0.1%
1056422
 
< 0.1%
10408402
 
< 0.1%
11239932
 
< 0.1%
3562142
 
< 0.1%
938892
 
< 0.1%
1530092
 
< 0.1%
6570552
 
< 0.1%
1149412
 
< 0.1%
Other values (9856)9978
99.8%
ValueCountFrequency (%)
2431
< 0.1%
5371
< 0.1%
5521
< 0.1%
5611
< 0.1%
6111
< 0.1%
6141
< 0.1%
7261
< 0.1%
8591
< 0.1%
13681
< 0.1%
14611
< 0.1%
ValueCountFrequency (%)
11987411
< 0.1%
11987351
< 0.1%
11986411
< 0.1%
11985531
< 0.1%
11984251
< 0.1%
11983611
< 0.1%
11982771
< 0.1%
11981341
< 0.1%
11981151
< 0.1%
11979791
< 0.1%

is_mobile
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8648 
1
1352 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

Length

2022-09-03T18:39:36.300242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T18:39:36.409580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

Most occurring characters

ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08648
86.5%
11352
 
13.5%

is_package
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7497 
1
2503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

Length

2022-09-03T18:39:36.506620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T18:39:36.619938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

Most occurring characters

ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07497
75.0%
12503
 
25.0%

channel
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8968
Minimum0
Maximum10
Zeros1224
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:36.711275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q39
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.707603592
Coefficient of variation (CV)0.6287484045
Kurtosis-1.521642298
Mean5.8968
Median Absolute Deviation (MAD)0
Skewness-0.5130586795
Sum58968
Variance13.74632439
MonotonicityNot monotonic
2022-09-03T18:39:36.823915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
95570
55.7%
01224
 
12.2%
11018
 
10.2%
2768
 
7.7%
5636
 
6.4%
3424
 
4.2%
4237
 
2.4%
776
 
0.8%
833
 
0.3%
613
 
0.1%
ValueCountFrequency (%)
01224
 
12.2%
11018
 
10.2%
2768
 
7.7%
3424
 
4.2%
4237
 
2.4%
5636
 
6.4%
613
 
0.1%
776
 
0.8%
833
 
0.3%
95570
55.7%
ValueCountFrequency (%)
101
 
< 0.1%
95570
55.7%
833
 
0.3%
776
 
0.8%
613
 
0.1%
5636
 
6.4%
4237
 
2.4%
3424
 
4.2%
2768
 
7.7%
11018
 
10.2%

srch_ci
Categorical

HIGH CARDINALITY

Distinct967
Distinct (%)9.7%
Missing8
Missing (%)0.1%
Memory size78.2 KiB
2014-12-26
 
69
2014-12-25
 
56
2014-12-27
 
49
2014-12-30
 
49
2014-12-28
 
49
Other values (962)
9720 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters99920
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)0.8%

Sample

1st row2014-08-12
2nd row2014-08-17
3rd row2013-05-09
4th row2014-08-11
5th row2014-08-07

Common Values

ValueCountFrequency (%)
2014-12-2669
 
0.7%
2014-12-2556
 
0.6%
2014-12-2749
 
0.5%
2014-12-3049
 
0.5%
2014-12-2849
 
0.5%
2014-12-3146
 
0.5%
2014-08-3042
 
0.4%
2014-12-2041
 
0.4%
2014-08-2939
 
0.4%
2014-12-1938
 
0.4%
Other values (957)9514
95.1%

Length

2022-09-03T18:39:36.942916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-12-2669
 
0.7%
2014-12-2556
 
0.6%
2014-12-2749
 
0.5%
2014-12-3049
 
0.5%
2014-12-2849
 
0.5%
2014-12-3146
 
0.5%
2014-08-3042
 
0.4%
2014-12-2041
 
0.4%
2014-08-2939
 
0.4%
2014-12-1938
 
0.4%
Other values (957)9514
95.2%

Most occurring characters

ValueCountFrequency (%)
021720
21.7%
-19984
20.0%
118789
18.8%
216130
16.1%
47778
 
7.8%
34934
 
4.9%
52906
 
2.9%
82100
 
2.1%
71953
 
2.0%
91836
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79936
80.0%
Dash Punctuation19984
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021720
27.2%
118789
23.5%
216130
20.2%
47778
 
9.7%
34934
 
6.2%
52906
 
3.6%
82100
 
2.6%
71953
 
2.4%
91836
 
2.3%
61790
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-19984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common99920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021720
21.7%
-19984
20.0%
118789
18.8%
216130
16.1%
47778
 
7.8%
34934
 
4.9%
52906
 
2.9%
82100
 
2.1%
71953
 
2.0%
91836
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII99920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021720
21.7%
-19984
20.0%
118789
18.8%
216130
16.1%
47778
 
7.8%
34934
 
4.9%
52906
 
2.9%
82100
 
2.1%
71953
 
2.0%
91836
 
1.8%

srch_co
Categorical

HIGH CARDINALITY

Distinct978
Distinct (%)9.8%
Missing8
Missing (%)0.1%
Memory size78.2 KiB
2015-01-01
 
60
2015-01-03
 
57
2014-12-28
 
55
2015-01-02
 
54
2014-12-29
 
53
Other values (973)
9713 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters99920
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.9%

Sample

1st row2014-08-13
2nd row2014-08-21
3rd row2013-05-10
4th row2014-08-13
5th row2014-08-08

Common Values

ValueCountFrequency (%)
2015-01-0160
 
0.6%
2015-01-0357
 
0.6%
2014-12-2855
 
0.5%
2015-01-0254
 
0.5%
2014-12-2953
 
0.5%
2015-01-0446
 
0.5%
2014-12-2745
 
0.4%
2014-09-1444
 
0.4%
2014-08-3143
 
0.4%
2014-12-2641
 
0.4%
Other values (968)9494
94.9%

Length

2022-09-03T18:39:37.067894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-01-0160
 
0.6%
2015-01-0357
 
0.6%
2014-12-2855
 
0.6%
2015-01-0254
 
0.5%
2014-12-2953
 
0.5%
2015-01-0446
 
0.5%
2014-12-2745
 
0.5%
2014-09-1444
 
0.4%
2014-08-3143
 
0.4%
2014-12-2641
 
0.4%
Other values (968)9494
95.0%

Most occurring characters

ValueCountFrequency (%)
022046
22.1%
-19984
20.0%
118743
18.8%
215867
15.9%
47713
 
7.7%
34795
 
4.8%
53049
 
3.1%
82103
 
2.1%
71929
 
1.9%
91928
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79936
80.0%
Dash Punctuation19984
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022046
27.6%
118743
23.4%
215867
19.8%
47713
 
9.6%
34795
 
6.0%
53049
 
3.8%
82103
 
2.6%
71929
 
2.4%
91928
 
2.4%
61763
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-19984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common99920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
022046
22.1%
-19984
20.0%
118743
18.8%
215867
15.9%
47713
 
7.7%
34795
 
4.8%
53049
 
3.1%
82103
 
2.1%
71929
 
1.9%
91928
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII99920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
022046
22.1%
-19984
20.0%
118743
18.8%
215867
15.9%
47713
 
7.7%
34795
 
4.8%
53049
 
3.1%
82103
 
2.1%
71929
 
1.9%
91928
 
1.9%

srch_adults_cnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0292
Minimum0
Maximum9
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:37.178738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9217550234
Coefficient of variation (CV)0.454245527
Kurtosis8.924681825
Mean2.0292
Median Absolute Deviation (MAD)0
Skewness2.258161018
Sum20292
Variance0.8496323232
MonotonicityNot monotonic
2022-09-03T18:39:37.286422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
26527
65.3%
12136
 
21.4%
3551
 
5.5%
4547
 
5.5%
695
 
0.9%
580
 
0.8%
828
 
0.3%
027
 
0.3%
77
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
027
 
0.3%
12136
 
21.4%
26527
65.3%
3551
 
5.5%
4547
 
5.5%
580
 
0.8%
695
 
0.9%
77
 
0.1%
828
 
0.3%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
828
 
0.3%
77
 
0.1%
695
 
0.9%
580
 
0.8%
4547
 
5.5%
3551
 
5.5%
26527
65.3%
12136
 
21.4%
027
 
0.3%

srch_children_cnt
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3341
Minimum0
Maximum6
Zeros7884
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:37.390873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7278256405
Coefficient of variation (CV)2.178466449
Kurtosis6.234488353
Mean0.3341
Median Absolute Deviation (MAD)0
Skewness2.40951153
Sum3341
Variance0.529730163
MonotonicityNot monotonic
2022-09-03T18:39:37.495732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
07884
78.8%
11128
 
11.3%
2811
 
8.1%
3128
 
1.3%
441
 
0.4%
55
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
07884
78.8%
11128
 
11.3%
2811
 
8.1%
3128
 
1.3%
441
 
0.4%
55
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
63
 
< 0.1%
55
 
0.1%
441
 
0.4%
3128
 
1.3%
2811
 
8.1%
11128
 
11.3%
07884
78.8%

srch_rm_cnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1157
Minimum0
Maximum8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:37.616592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4683326206
Coefficient of variation (CV)0.4197657261
Kurtosis69.34542885
Mean1.1157
Median Absolute Deviation (MAD)0
Skewness6.861619633
Sum11157
Variance0.2193354435
MonotonicityNot monotonic
2022-09-03T18:39:37.732846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
19150
91.5%
2673
 
6.7%
3116
 
1.2%
425
 
0.2%
518
 
0.2%
810
 
0.1%
66
 
0.1%
01
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
19150
91.5%
2673
 
6.7%
3116
 
1.2%
425
 
0.2%
518
 
0.2%
66
 
0.1%
71
 
< 0.1%
810
 
0.1%
ValueCountFrequency (%)
810
 
0.1%
71
 
< 0.1%
66
 
0.1%
518
 
0.2%
425
 
0.2%
3116
 
1.2%
2673
 
6.7%
19150
91.5%
01
 
< 0.1%

srch_destination_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct2571
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14524.2294
Minimum8
Maximum64940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:37.874006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile1612.1
Q18266
median9147
Q319257.25
95-th percentile42630
Maximum64940
Range64932
Interquartile range (IQR)10991.25

Descriptive statistics

Standard deviation11164.01188
Coefficient of variation (CV)0.7686474489
Kurtosis3.861588013
Mean14524.2294
Median Absolute Deviation (MAD)2861.5
Skewness1.896025874
Sum145242294
Variance124635161.2
MonotonicityNot monotonic
2022-09-03T18:39:38.033091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8250344
 
3.4%
8267265
 
2.6%
8791177
 
1.8%
8268141
 
1.4%
8253138
 
1.4%
8745122
 
1.2%
8260100
 
1.0%
825498
 
1.0%
1143993
 
0.9%
827990
 
0.9%
Other values (2561)8432
84.3%
ValueCountFrequency (%)
81
 
< 0.1%
191
 
< 0.1%
212
< 0.1%
241
 
< 0.1%
253
< 0.1%
272
< 0.1%
331
 
< 0.1%
401
 
< 0.1%
432
< 0.1%
591
 
< 0.1%
ValueCountFrequency (%)
649401
< 0.1%
647781
< 0.1%
646271
< 0.1%
646231
< 0.1%
645951
< 0.1%
642921
< 0.1%
639502
< 0.1%
638941
< 0.1%
636872
< 0.1%
636421
< 0.1%

srch_destination_type_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5915
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:38.175197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.157659218
Coefficient of variation (CV)0.8325908615
Kurtosis-1.174449517
Mean2.5915
Median Absolute Deviation (MAD)0
Skewness0.7812028658
Sum25915
Variance4.655493299
MonotonicityNot monotonic
2022-09-03T18:39:38.282795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16168
61.7%
62256
 
22.6%
3733
 
7.3%
5478
 
4.8%
4324
 
3.2%
839
 
0.4%
72
 
< 0.1%
ValueCountFrequency (%)
16168
61.7%
3733
 
7.3%
4324
 
3.2%
5478
 
4.8%
62256
 
22.6%
72
 
< 0.1%
839
 
0.4%
ValueCountFrequency (%)
839
 
0.4%
72
 
< 0.1%
62256
 
22.6%
5478
 
4.8%
4324
 
3.2%
3733
 
7.3%
16168
61.7%

is_booking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9259 
1
 
741

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

Length

2022-09-03T18:39:38.407617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-03T18:39:38.517035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

Most occurring characters

ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09259
92.6%
1741
 
7.4%

cnt
Real number (ℝ≥0)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4943
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:38.616130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum39
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.260922877
Coefficient of variation (CV)0.8438217743
Kurtosis146.6650986
Mean1.4943
Median Absolute Deviation (MAD)0
Skewness7.930282349
Sum14943
Variance1.589926503
MonotonicityNot monotonic
2022-09-03T18:39:38.736303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
17391
73.9%
21549
 
15.5%
3557
 
5.6%
4222
 
2.2%
5128
 
1.3%
655
 
0.5%
735
 
0.4%
827
 
0.3%
911
 
0.1%
107
 
0.1%
Other values (9)18
 
0.2%
ValueCountFrequency (%)
17391
73.9%
21549
 
15.5%
3557
 
5.6%
4222
 
2.2%
5128
 
1.3%
655
 
0.5%
735
 
0.4%
827
 
0.3%
911
 
0.1%
107
 
0.1%
ValueCountFrequency (%)
391
 
< 0.1%
341
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
164
< 0.1%
141
 
< 0.1%
133
< 0.1%
122
 
< 0.1%
114
< 0.1%
107
0.1%

hotel_continent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1641
Minimum0
Maximum6
Zeros186
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:38.853954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.629121418
Coefficient of variation (CV)0.5148767162
Kurtosis-0.7073179295
Mean3.1641
Median Absolute Deviation (MAD)0
Skewness0.7745211142
Sum31641
Variance2.654036594
MonotonicityNot monotonic
2022-09-03T18:39:38.951478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
25261
52.6%
62029
 
20.3%
31270
 
12.7%
41135
 
11.3%
0186
 
1.9%
5119
 
1.2%
ValueCountFrequency (%)
0186
 
1.9%
25261
52.6%
31270
 
12.7%
41135
 
11.3%
5119
 
1.2%
62029
 
20.3%
ValueCountFrequency (%)
62029
 
20.3%
5119
 
1.2%
41135
 
11.3%
31270
 
12.7%
25261
52.6%
0186
 
1.9%

hotel_country
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.9076
Minimum0
Maximum208
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:39.096308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q150
median50
Q3105
95-th percentile198
Maximum208
Range208
Interquartile range (IQR)55

Descriptive statistics

Standard deviation56.28936411
Coefficient of variation (CV)0.6957240618
Kurtosis-0.1373187037
Mean80.9076
Median Absolute Deviation (MAD)3
Skewness1.049365285
Sum809076
Variance3168.492511
MonotonicityNot monotonic
2022-09-03T18:39:39.255212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504789
47.9%
8503
 
5.0%
198472
 
4.7%
105344
 
3.4%
70321
 
3.2%
204296
 
3.0%
77260
 
2.6%
182220
 
2.2%
106158
 
1.6%
144145
 
1.5%
Other values (123)2492
24.9%
ValueCountFrequency (%)
025
 
0.2%
14
 
< 0.1%
33
 
< 0.1%
43
 
< 0.1%
580
 
0.8%
724
 
0.2%
8503
5.0%
94
 
< 0.1%
101
 
< 0.1%
119
 
0.1%
ValueCountFrequency (%)
20876
 
0.8%
2061
 
< 0.1%
2051
 
< 0.1%
204296
3.0%
20326
 
0.3%
2025
 
0.1%
2009
 
0.1%
19917
 
0.2%
198472
4.7%
19645
 
0.4%

hotel_market
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1108
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.8479
Minimum2
Maximum2113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:39.415751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q1155
median585
Q3701
95-th percentile1614
Maximum2113
Range2111
Interquartile range (IQR)546

Descriptive statistics

Standard deviation508.7806767
Coefficient of variation (CV)0.853876764
Kurtosis0.1998705827
Mean595.8479
Median Absolute Deviation (MAD)360
Skewness0.972189318
Sum5958479
Variance258857.7769
MonotonicityNot monotonic
2022-09-03T18:39:39.573210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
628458
 
4.6%
675431
 
4.3%
365228
 
2.3%
682228
 
2.3%
701224
 
2.2%
19221
 
2.2%
110211
 
2.1%
27172
 
1.7%
368135
 
1.4%
212128
 
1.3%
Other values (1098)7564
75.6%
ValueCountFrequency (%)
298
1.0%
33
 
< 0.1%
444
0.4%
517
 
0.2%
68
 
0.1%
79
 
0.1%
818
 
0.2%
92
 
< 0.1%
1022
 
0.2%
116
 
0.1%
ValueCountFrequency (%)
21131
 
< 0.1%
21115
0.1%
21071
 
< 0.1%
21042
 
< 0.1%
21032
 
< 0.1%
21001
 
< 0.1%
20991
 
< 0.1%
20971
 
< 0.1%
20923
< 0.1%
20901
 
< 0.1%

hotel_cluster
Real number (ℝ≥0)

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.0406
Minimum0
Maximum99
Zeros94
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-09-03T18:39:39.733341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median50
Q373
95-th percentile96
Maximum99
Range99
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.69762546
Coefficient of variation (CV)0.5734868379
Kurtosis-1.125432989
Mean50.0406
Median Absolute Deviation (MAD)23
Skewness0.00430155984
Sum500406
Variance823.553707
MonotonicityNot monotonic
2022-09-03T18:39:39.907230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91276
 
2.8%
41212
 
2.1%
48191
 
1.9%
64185
 
1.8%
65177
 
1.8%
5173
 
1.7%
83169
 
1.7%
98161
 
1.6%
21153
 
1.5%
50152
 
1.5%
Other values (90)8151
81.5%
ValueCountFrequency (%)
094
0.9%
195
0.9%
291
0.9%
369
 
0.7%
492
0.9%
5173
1.7%
6103
1.0%
778
0.8%
895
0.9%
9132
1.3%
ValueCountFrequency (%)
99141
1.4%
98161
1.6%
97139
1.4%
96104
 
1.0%
95121
1.2%
9475
 
0.8%
9336
 
0.4%
9253
 
0.5%
91276
2.8%
9095
 
0.9%

Interactions

2022-09-03T18:39:29.269873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:51.450126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:53.942839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:55.955830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:57.971291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.181973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.277744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.346740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:06.533453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:08.468682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:10.658572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.109291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.141463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:18.664717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.082884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.273174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:26.734387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:29.395271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:51.613652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.055083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.072263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.085154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.312397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.396222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.456662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:06.641718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:08.588442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:10.804906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.246271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.300311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:18.800567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.202099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.386114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:26.908254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:29.519352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:51.746021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.169735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.186318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.205766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.426902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.514724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.568342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:06.752370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:08.707588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:10.942769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.387369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.478144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:18.937776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.340057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.508206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:27.108675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:29.651011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:51.915845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.289805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.309333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.326384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.540459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.634366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.680109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:06.867491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:08.830370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:11.079602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.517730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.672252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:19.078085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.460220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.661014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:27.226582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:29.860580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:52.062195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.404499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.425112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.443647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.658757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.753491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.794846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:06.977417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:08.948509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:11.216225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.664850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.828845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:19.218585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.617735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.837454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:27.338671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:30.077938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:52.316440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.522676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.541982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.563768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.786787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:02.879626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:04.911360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:07.096894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:09.089001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:11.351622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.824451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:16.971355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:19.354241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.787266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:24.965444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:27.454232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:30.245329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:52.441910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.643310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.663329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.865601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:00.910410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:03.005678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:05.031329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:07.212661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:09.216053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:11.486641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:13.991164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:17.106171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:19.493032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:21.963762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:25.093778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:27.572184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:30.457769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:52.556007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:54.762158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:56.782074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:38:58.981175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:01.026591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-09-03T18:39:03.122842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2022-09-03T18:39:40.095971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-03T18:39:40.398492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-03T18:39:41.130550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-03T18:39:41.453674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-03T18:39:41.621473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-03T18:39:32.244814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-03T18:39:33.084147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-03T18:39:33.450889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-03T18:39:33.616452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

date_timesite_nameposa_continentuser_location_countryuser_location_regionuser_location_cityorig_destination_distanceuser_idis_mobileis_packagechannelsrch_cisrch_cosrch_adults_cntsrch_children_cntsrch_rm_cntsrch_destination_idsrch_destination_type_idis_bookingcnthotel_continenthotel_countryhotel_markethotel_cluster
02014-07-03 10:19:153432053542731525.951410465390092014-08-122014-08-134024260410325067372
12014-07-02 18:39:51343205339211411332.29453049701052014-08-172014-08-212011749411125042591
22013-05-09 09:41:01332664423539017.087911865310092013-05-092013-05-101011255650225040916
32014-07-30 19:23:012366442124161990.14192898060092014-08-112014-08-132118854101219840274
42014-07-10 15:14:4825219815317956NaN8653570092014-08-072014-08-082012304810134815216
52013-09-07 18:28:492366442462961206.305411221221192013-10-172013-10-19201825410125036517
62013-07-12 11:30:40236622010345NaN3967690192013-07-142013-07-16101825010125062849
72013-08-28 03:52:04232351353632NaN5397730092013-09-022013-09-0510121486601620099638
82013-02-06 14:44:532366348488625008.890411318470092013-03-162013-03-20201885710125021428
92014-04-06 05:59:13236629479761397.3395426171192014-06-042014-06-093011220660225062818

Last rows

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99902014-12-11 08:27:3823662207755119.0050108970102014-12-192014-12-262211848860161052981
99912014-07-12 10:44:1824237911049NaN4668160052014-08-102014-08-11101874110161441337
99922014-12-25 10:30:59236626024757985.81157635180002015-02-122015-02-152011226160125041291
99932014-12-31 07:02:28236625435291096.96604025920022015-04-162015-04-192011220660125062879
99942013-11-29 17:28:2823193847318NaN3587180092013-12-302014-01-03101828710221983976
99952014-10-16 01:07:25231292126044NaN4385000112014-10-172014-10-2220111193014137209262
99962013-06-01 20:42:07236631124240NaN3677341002013-06-082013-06-102011409941125063795
99972013-03-02 10:21:52343205135276551169.26003249021092013-03-122013-03-1820112269601250123041
99982013-06-12 08:58:4424235022013NaN6815440092013-07-122013-07-16101880810161692882
99992013-12-09 17:07:5623573425021NaN10360990192014-03-282014-04-01201826810225068277